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Salient Bag of Feature for Skin Lesion Recognition

Volume 15, Number 4, April 2019, pp. 1083-1093
DOI: 10.23940/ijpe.19.04.p3.10831093

Pawan Kumar Upadhyay and Satish Chandra

Department of Computer Science and Engineering, JIIT, Noida, 201301, India

(Submitted on July 10, 2018; Revised on November 10, 2018; Accepted on March 15, 2019)


With the rapidly increasing incidence of various types of skin cancer, there is a need for decision support systems to detect abnormalities in the early stages and help reduce the mortality rate. Several computer-aided diagnosis (CAD) systems have been proposed in the last two decades for skin melanoma recognition. Continuous improvements have been made in the accuracy of melanoma diagnosis, but other classes of cancer, such as basal cell carcinoma and squamous cell carcinoma, are not very intact with the non-invasive diagnosis system. In this paper, a generic method of diagnostic system is proposed and is viable to classify the ten classes of a skin lesion. These lesion classes belong to cancer, pre-cancerous, and tumor categories of samples, as shown in a gold standard image dataset. The key idea of the proposed approach is to optimize the bag-of-SURF features by the non-linear Hessian matrix of HSV color descriptors. These features are combined to form a salient bag-of-features, which helps recognize the skin lesion classes more accurately. Experimental results show that the proposed method of skin lesion diagnosis significantly improves the accuracy of recognition up to 89% as compared to the current state-of-the-art accuracy of 81.8%. It does not require any complex pre-processing of images, which affects the performance of the recognition system.

References: 25

    1. D. S. Rigel, J. Russak, and R. Friedman, “The Evolution of Melanoma Diagnosis: 25 Years Beyond the ABCDs,” CA: A Cancer Journal for Clinicians, Vol. 60, No. 5, pp. 301-316, 2010
    2. T. Wadhawan, N. Situ, H. Rui, K. Lancaster, X. Yuan, and G. Zouridakis, “Implementation of the 7-Point Checklist for Melanoma Detection on Smart Handheld Devices,” in Proceedings of 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3180-3183, 2011
    3. R. Marks, “Epidemiology of Non-Melanoma Skin Cancer and Solar Keratosis in Australia: A Tale of Self-Immolation in Elysian Fields,” The Australasian Journal of Dermatology, Vol. 38, pp. 26-29, June 1997
    4. C. Barata, M. Ruela, M. Francisco, T. Mendonça, and J. S. Marques, “Two Systems for the Detection of Melanomas in Dermoscopy Images using Texture and Color Features,” IEEE Systems Journal, Vol. 8, No. 3, pp. 965-979, 2014
    5. P. J. Quaedvlieg, E. Tirisi, M. R. Thissen, and G. A. Krekels, “Actinic Keratosis: How to Differentiate the Good from the Bad Ones,” European Journal of Dermatology, Vol. 16, No. 4, pp. 335-339, 2006
    6. R. R. Neto and B. B. Yates, “Modern Information Retrieval,” 1st Edition, Addison Wesley, 1999
    7. N. Situ, X. Yuan, J. Chen, and G. Zouridakis, “Malignant Melanoma Detection by Bag-of-Features Classification,” in Proceedings of 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3110-3113, 2008
    8. C. Barata, J. Marques, and T. Mendonca, “Bag-of-Features Classification Model for the Diagnose of Melanoma in Dermoscopy Images using Color and Texture Descriptors,” in Proceedings of International Conference on Image Analysis and Recognition (ICIAR), pp. 547-555, 2013
    9. J. Sivic and A. Zisserman, “Video Google: A Text Retrieval Approach to Object Matching in Videos,” in Proceedings of the Ninth IEEE International Conference on Computer Vision, Vol. 2, 2013
    10. Y. Boureau, F. Bach, Y. LeCun, and J. Ponce, “Learning Mid-Level Features for Recognition,” in Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2559-2566, 2010
    11. H. Iyatomi, G. Schaefer, W. V. Stoecker, and M. E. Celebi, “Lesion Border Detection in Dermoscopy Images,” Computerized Medical Imaging and Graphics, Vol. 33, No. 2, pp. 148-153, 2009
    12. H. Bay, T. Tuytelaars, and L. V. Gool, “Speeded-Up Robust Features (SURF),” in Proceedings of European Conference on Computer vision, pp. 404-417, 2006
    13. L. Ballerini, R. B. Fisher, B. Aldridge, and J. Rees, “A Color and Texture based Hierarchical K-NN Approach to the Classification of Non-Melanoma Skin Lesions,” Color Medical Image Analysis, pp. 63-86, 2013
    14. S. Gupta, S. K. Chakarvarti, and Zaheeruddin, “Medical Image Registration based on Fuzzy C-Means Clustering Segmentation Approach using SURF,” International Journal of Biomedical Engineering and Technology, Vol. 20, No. 1, pp. 33-50, 2016
    15. M. Rastgoo, R. Garcia, O. Morel, and F. Marzani, “Automatic Differentiation of Melanoma from Dysplastic Nevi,” Computerized Medical Imaging and Graphics, Vol. 43, pp. 44-52, 2015
    16. H. Bay, T. Tuytelaars, and L. V. Gool, “Speeded-up Robust Features (SURF),” Computer Vision and Image Understanding, Vol. 110, No. 3, pp. 346-359, June 2008
    17. H. Iyatomi, H. Oka, M. E. Celebi, M. Hashimoto, M. Hagiwara, and M. T. K. Ogawa, “An Improved Internet-based Melanoma Screening System with Dermatologist-Like Tumor Area Extraction Algorithm,” Computerized Medical Imaging and Graphics Journal, Vol. 32, No. 7, pp. 566-579, 2008
    18. M. Ruela, M. Francisco, and C. Barata, “Two Systems for the Detection of Melanomas in Dermoscopy Images using Texture and Color Features,” IEEE Systems Journal, Vol. 8, No. 3, pp. 965-979, 2014
    19. M. E. Celebi, H. A. Kingravi, B. Uddin, H. Iyatomi, Y. A. Aslandogan, W. V. Stoecker, et al., “A Methodological Approach to the Classification of Dermoscopy Images,” Computerized Medical Imaging and Graphics, Vol. 31, No. 6, pp. 362-373, 2007
    20. J. Luo, M. Boutell, and C. Brown, “Pictures are Not Taken in a Vacuum-an Overview of Exploiting Context for Semantic Scene Content Understanding,” Signal Processing Magazine, Vol. 23, pp. 101-114, 2006
    21. T. Hofmann, “Unsupervised Learning by Probabilistic Latent,” Machine Learning Journal, Vol. 42, pp. 177-196, 2001
    22. D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent Dirichlet Allocation,” Machine Learning Research, Vol. 3, pp. 993-1022, 2003
    23. Y. Sun and S. OZawa, “Semantic-Meaningful Content-based Image Retrieval in Wavelet Domain,” in Proceedings of the 5th ACM  SIGMM International Workshop on Multimedia Information Retrieval, pp. 122-129, 2003
    24. Y. Chen and J. Z. Wang, “A Region-based Fuzzy Feature Matching Approach to Content-based Image Retrieval,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, pp. 1252-1267, 2002
    25. J. Kawahara, A. BenTaieb, and G. Hamarneh, “Deep Features to Classify Skin Lesions,” in Proceedings of IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 1397-1400, 2016


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